code: def load_tokens_from_pretrained_token_embeddings(parameters):
      file_input = codecs.open(
          parameters["token_pretrained_embedding_filepath"],
          "r",
          "UTF-8",
      )
      count = -1
      tokens = set()
      number_of_loaded_word_vectors = 0
      for cur_line in file_input:
          count += 1
          cur_line = cur_line.strip()
          cur_line = cur_line.split(" ")
          if len(cur_line) == 0:
              continue
          token = cur_line[0]
          tokens.add(token)
          number_of_loaded_word_vectors += 1
      file_input.close()
      return tokens
  
file_path: NeuroNER/neuroner/utils_nlp.py
imports: import codecs
  import os
  import re
  import numpy
  from neuroner import utils
label_desc: Use image node_id 1 for calling a global function with example usage: load_tokens_from_pretrained_token_embeddings(parameters) and returns: tokens